The random survival forests (RSFs) method [1]} is an extension of random forests that supports the analysis of right-censored data. The training procedure of RSFs is similar to other random forests. However, the branching rule is modified to account for right-censored data by measuring the survival difference between the samples on either side of the split, i.e., survival times. RSFs have become popular as a non-parametric alternative to CPH due to their less restrictive model assumptions. However, similar to CPH, RSFs cannot be trained on longitudinal datasets.
